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Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Author

Listed:
  • Md Shafiullah

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • M. A. Abido

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Taher Abdel-Fattah

    (Electrical Engineering Department, Menoufia University, Shebin El-Kom 32511, Egypt)

Abstract

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R 2 ), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

Suggested Citation

  • Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2328-:d:167619
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    References listed on IDEAS

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    1. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
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    Cited by:

    1. Hamid Mirshekali & Rahman Dashti & Karsten Handrup & Hamid Reza Shaker, 2021. "Real Fault Location in a Distribution Network Using Smart Feeder Meter Data," Energies, MDPI, vol. 14(11), pages 1-16, June.
    2. Khaled J. Assi & Md Shafiullah & Kh Md Nahiduzzaman & Umer Mansoor, 2019. "Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    3. Muhammad Muhitur Rahman & Md Shafiullah & Syed Masiur Rahman & Abu Nasser Khondaker & Abduljamiu Amao & Md. Hasan Zahir, 2020. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future," Sustainability, MDPI, vol. 12(10), pages 1-33, May.
    4. Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.
    5. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.

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